class_docstr: A simple 2D point environment.
  
  Args:
      goal (np.ndarray): A 2D array representing the goal position
      arena_size (float): The size of arena where the point is constrained
          within (-arena_size, arena_size) in each dimension
      done_bonus (float): A numerical bonus added to the reward
          once the point as reached the goal
      never_done (bool): Never send a `done` signal, even if the
          agent achieves the goal
      max_episode_length (int): The maximum steps allowed for an episode.
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docstr: Reset the environment.
  
  Returns:
      numpy.ndarray: The first observation conforming to
          `observation_space`.
      dict: The episode-level information.
          Note that this is not part of `env_info` provided in `step()`.
          It contains information of he entire episode， which could be
          needed to determine the first action (e.g. in the case of
          goal-conditioned or MTRL.)
docstr_tok: ["Reset","the","environment",".","Returns",":","numpy.ndarray",":","The","first","observation","conforming","to","`","observation_space","`",".","dict",":","The","episode-level","information",".","Note","that","this","is","not","part","of","`","env_info","`","provided","in","`","step","(",")","`",".","It","contains","information","of","he","entire","episode\uff0c","which","could","be","needed","to","determine","the","first","action","(","e.g",".","in","the","case","of","goal-conditioned","or","MTRL",".",")"]
code: def reset(self):
      """Reset the environment.
  
      Returns:
          numpy.ndarray: The first observation conforming to
              `observation_space`.
          dict: The episode-level information.
              Note that this is not part of `env_info` provided in `step()`.
              It contains information of he entire episode， which could be
              needed to determine the first action (e.g. in the case of
              goal-conditioned or MTRL.)
  
      """
      self._point = np.zeros_like(self._goal)
      dist = np.linalg.norm(self._point - self._goal)
  
      first_obs = np.concatenate([self._point, (dist,)])
      self._step_cnt = 0
  
      return first_obs, dict(goal=self._goal)
  
file_path: garage/src/garage/envs/point_env.py
imports: import math
  import akro
  import numpy
  from garage import Environment, EnvSpec, EnvStep, StepType
label_desc: Use image node_id 6 for calling the PointEnv obj's underlying member method code with example usage: obj.reset() and returns: first_obs,  dict